In this paper, we use support vector machines (SVM) to develop a machine learning framework to discover phase space structures that distinguish between distinct reaction pathways. The SVM model is trained using data from trajectories of Hamilton's equations and works well even with relatively few trajectories. Moreover, this framework is specifically designed to require minimal a priori knowledge of the dynamics in a system. This makes our approach computationally better suited than existing methods for high-dimensional systems and systems where integrating trajectories is expensive. We benchmark our approach on Chesnavich's CH$_4^+$ Hamiltonian.
翻译:在本文中,我们使用辅助矢量机(SVM)开发一个机器学习框架,以发现区分不同反应路径的相位空间结构。SVM模型使用汉密尔顿方程式轨迹数据进行了培训,而且即使在相对较少的轨迹下也运作良好。此外,这一框架的具体设计是要求至少先验地了解一个系统中的动态。这使得我们的方法在计算上比现有的高维系统方法和整合轨迹费用昂贵的系统更适合。我们以切斯纳维奇的CH$_4 ⁇ $汉密尔顿仪为基准。